Complex event processing (CEP) systems have been known as a technique to process big data that are momentarily collected from various objects. The CEP engine, for example, performs processing (hereinafter also referred to as “event processing”) on multiple data items received from various objects in accordance with a predetermined rule, and successively outputs the results of the event processing to an output destination.
The big data include data from which effective data to be output to the output destination are not extractable by the event processing. That is, the big data include data that are necessary and data that are unnecessary to the CEP system. Data from which effective data to be output to the output destination are not extractable do not have to be input to the event processing.
In response to this, if a manager manually sorts necessary data from unnecessary data, the manager's work increases to increase an operational cost. Furthermore, because the sorting of data depends on the manager's personal skill, it is possible that necessary data and unnecessary data are not properly filtered so that an operational load on the system remains high.
Therefore, a technique to monitor event processing on events of individual event types stored in a reception buffer and reduce the number of events to be stored in the reception buffer based on the degree of importance of each event type determined based on the result of the monitoring has been known. (See, for example, Japanese Laid-Open Patent Publication No. 2012-118928.)